Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of...

Descripción completa

Detalles Bibliográficos
Autores principales: Ghaderi, Parviz, Nosouhi, Marjan, Jordanic, Mislav, Marateb, Hamid Reza, Mañanas, Miguel Angel, Farina, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959430/
https://www.ncbi.nlm.nih.gov/pubmed/35356057
http://dx.doi.org/10.3389/fnins.2022.796711

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959430/
https://www.ncbi.nlm.nih.gov/pubmed/35356057
http://dx.doi.org/10.3389/fnins.2022.796711

Ejemplares similares